Calling absolute copy numberΒΆ

The relationship between the observed copy ratio and the true underlying copy number depends on tumor cell fraction (purity), genome ploidy (which may be heterogeneous in a tissue sample), and the size of the subclonal population containing the CNA. Because of these ambiguities, CNVkit only reports the estimated log2 copy ratio, and does not currently attempt a formal statistical test for estimating integer copy number.

In a diploid genome, a single-copy gain in a perfectly pure, homogeneous sample has a copy ratio of 3/2. In log2 scale, this is log2(3/2) = 0.585, and a single-copy loss is log2(1/2) = -1.0.

In the diagram plot, for the sake of providing a clean visualization of confidently called CNAs, the default threshold to label genes is 0.5. This threshold will tend to display gene amplifications, fully clonal single-copy gains in fairly pure samples, most single-copy losses, and complete deletions.

When using the gainloss command, choose a threshold to suit your needs depending on your knowledge of the sample’s purity, heterogeneity, and likely features of interest. As a starting point, try 0.1 or 0.2 if you are going to do your own filtering downstream, or 0.3 if not.

The call command implements two simple methods to convert the log2 ratios in a segmented .cns file to absolute integer copy number values. The segmetrics command computes segment-level summary statistics that can be used to evaluate the reliability of each segment. Future releases of CNVkit will integrate further statistical testing to help make meaningful variant calls from the log2 copy ratio data.

After using rescale and/or call, the adjusted .cns file can then be converted to BED or VCF format using the export command. These output styles display the inferred absolute integer copy number value of each segment.